Back propagation is one of the supervised learning and multi-layered training program and uses errors during the process of changing the weight value in the backward process as well as the forward propagation. In the method for predicting cognitive abilities backpropagation the first step is to set the input neuron number, the number of neurons that are hidden, and the number of output neurons. The number of neurons used in the program is 6 neurons consisting of cognitive criteria, 6 hidden neuron layers, and 2 neuron outputs. The highest level of accuracy is in the binary sigmoid and bipolar sigmoid activation functions at the 64th epoch with the accuracy of each function of 82.93% +/- 37.63% and 85.37% +/- 35.34%. The smallest root mean squared error value was found in binary sigmoid of 0.266 with a tolerance of +/- 0.258 on the 100th epoch with an accuracy of 80.49% while for the sigmoid bipolar activation function the smallest root mean squared error value was obtained at the epoch 500 of 0.282 with tolerance +/- 0.353.
Password-Based Encryption gets the encryption key from the password. To make the task from keyword to key is very time-consuming for an attacker, most implementations of Password-based Encryption will be combined with a randomization system, known as salt. It should be that we want to effectively select an encryption key. We may want to encrypt files based on the passwords we enter, so we can remember them. In this case, the only information would be a password. Password-Based Encryption is used in the application because usually the attacker repeatedly tries to guess undetected keywords and is beyond the original sender / recipient control, if the keyword is used to log in to the server, it can detect many possibilities that are not properly done and in the worst case is to shut down the server to prevent more effort, if a tapper take encrypted files that we use. Password Based Encryption with Message Digest (MD5) and Data Encryption Standard (DES) is a cryptographic method using algorithms that combine both hashing and standard encryption methods. MD5 was developed by Ronald Rivest where the MD5 takes messages of any length and generates a 128-bit message digest, and with the use of DES working in plaintext it is useful to return the same size ciphertext.
Centroid is the central point of data in the grouping process, it is necessary to analyze the centroid in determining the initial value in the initial clustering process. So it is used as a cluster center point in the X-Means algorithm clustering process. Determine cluster center points or centroid, measure the performance of the X-Means algorithm with range cluster parameters by measuring distances between centroid for a fast and efficient way to group unstructured data, and to speed up the model construction process and divide several centroid in half to match the data as a test tool for the analysis of the X-Means method. From testing using the X-Means algorithm with the determination of the number of Centroid clusters carried out by modifying the X-Means method to do some determination of the centroid to get the results of 11 iterations. From the results of these tests produce good cluster members the level of similarity of data with other data and in determining the number of clusters, using the modification of the Euclidean distance method, get better results of the similarity level of each member compared to randomly determining the number of clusters with several iterations.
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